Transforms survival times using the cumulative hazard function.
vector of nonnegative survival times
vector of censoring indicators, should be the same length
A vector of transformed survival times.
The primary use of doing a cumulative hazard transformation is that after such a transformation, exponential survival models yield results that are often very much comparable to proportional hazards models. In our implementation of Logic Regression, however, exponential survival models run much faster than proportional hazards models when there are no continuous separate covariates.
Ruczinski I, Kooperberg C, LeBlanc ML (2003). Logic Regression, Journal of Computational and Graphical Statistics, 12, 475-511.
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